In this technical demonstration, we propose a cloud-based Big Data Platform for Social Multimedia Analytics called bBridge that automatically detects and profiles meaningful user communities in a specified geographical region, followed by rich analytics on communities’ multimedia streams. The system executes a community detection approach that considers the ability of social networks to complement each other during the process of latent representation learning, while the community profiling is implemented based on the state-of-the-art multi-modal latent topic modeling and personal user profiling techniques. The stream analytics is performed via cloud-based stream analytics engine, while the multi-source data crawler deployed as a distributed cloud jobs. Overall, the bBridge platform integrates all the above techniques to serve both business and personal objectives.
User profile learning, such as mobility and demographic profile learning, is of great importance to various applications. Meanwhile, the rapid growth of multiple social platforms makes it possible to perform a comprehensive user profile learning from different views. However, the research efforts on user profile learning from multiple data sources are still relatively sparse, and there is no large-scale dataset released towards user profile learning. In our study, we contribute such benchmark and perform an initial study on user mobility and demographic profile learning. First, we constructed and released a large-scale multi-source multimodal dataset from three geographical areas. We then applied our proposed ensemble model on this dataset to learn user profile. Based on our experimental results, we observed that multiple data sources mutually complement each other and their appropriate fusion boosts the user profiling performance.
Venue category recommendation is an essential application for the tourism and advertisement industries, wherein it may suggest attractive localities within close proximity to users’ current location. Considering that many adults use more than three social networks simultaneously, it is reasonable to leverage on this rapidly growing multi-source social media data to boost venue recommendation performance. Another approach to achieve higher recommendation results is to utilize group knowledge, which is able to diversify recommendation output. Taking into account these two aspects, we introduce a novel cross-network collaborative recommendation framework C 3R, which utilizes both individual and group knowledge, while being trained on data from multiple social media sources. Group knowledge is derived based on new crosssource user community detection approach, which utilizes both inter-source relationship and the ability of sources to complement each other. To fully utilize multi-source multi-view data, we process user-generated content by employing state-of-the-art text, image, and location processing techniques. Our experimental results demonstrate the superiority of our multi-source framework over state-of-the-art baselines and different data source combinations. In addition, we suggest a new approach for automatic construction of inter-network relationship graph based on the data, which eliminates the necessity of having pre-defined domain knowledge
Российские ученые и их коллеги из Сингапура решили эту проблему, создав систему машинного обучения, способную "вычислять" некоторые черты личности и пол игроков по данным из игровой платформы Steam.
Для создания и обучения этого искусственного интеллекта Самборский и его коллеги воспользовались данными видеостримингового сервиса Player.me, который объединял профили игроков в Steam с их учетными записями в Twitter, Facebook и Instagram. Сопоставляя манеру их игры с тем, что писали и как себя вели игроки в соцсетях, ученые раскрыли несколько любопытных связей между игровым поведением и особенностями личности человека.